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3 years ago

The coordinate of point T are (3,-11). The midpoint of line ST is (3,-9). Find the coordinates of point S

Mathematics

1 answer:

svetlana [45]3 years ago

5 0

Answer:

(3,-7)

Step-by-step explanation:

Midpoint Formula

(3,-9) = ((3+x)/2 , (-11+x)/2)

Solve for x coordinate

$3 = \frac{3 + x}{2} \\ 6 = 3 + x \\ 3 = x$

Solve for y coordinate

$- 9 = \frac{ - 11 + x}{2} \\ - 18 = - 11 + x \\ - 7 = y$

Therefore point s is (3, -7)

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Makovka662 [10]

Answer:

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3 years ago

Which of the following are measurements of the sides of a right triangle?

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2 years ago

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Write the equation of the line that goes through (-2, 0) and (4,3).

Snezhnost [94]

Answer:

Step-by-step explanation:

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2 years ago

Is floor(x) periodic?

diamong [38]

If you look at the graph of y = floor(x), you'll see a stairstep pattern that climbs up as you read from left to right. There are no vertical components to the graph. There are only horizontal components.

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Conclusion: The function floor(x) is <u>not</u> periodic.

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3 years ago

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Suppose that 20% of the residents in a certain state support an increase in the property tax. An opinion poll will randomly samp

aleksklad [387]

Answer:

95.44% probability the resulting sample proportion is within .04 of the true proportion.

Step-by-step explanation:

To solve this question, we need to understand the normal probability distribution and the central limit theorem.

Normal probability distribution

Problems of normally distributed samples are solved using the z-score formula.

In a set with mean $\mu$ and standard deviation $\sigma$ , the zscore of a measure X is given by:

$Z = \frac{X - \mu}{\sigma}$

The Z-score measures how many standard deviations the measure is from the mean. After finding the Z-score, we look at the z-score table and find the p-value associated with this z-score. This p-value is the probability that the value of the measure is smaller than X, that is, the percentile of X. Subtracting 1 by the pvalue, we get the probability that the value of the measure is greater than X.

Central Limit Theorem

The Central Limit Theorem estabilishes that, for a normally distributed random variable X, with mean $\mu$ and standard deviation $\sigma$ , the sampling distribution of the sample means with size n can be approximated to a normal distribution with mean $\mu$ and standard deviation $s = \frac{\sigma}{\sqrt{n}}$ .

For a skewed variable, the Central Limit Theorem can also be applied, as long as n is at least 30.

For the sampling distribution of the sample proportion in sample of size n, the mean is $\mu = p$ and the standard deviation is $s = \sqrt{\frac{p(1-p)}{n}}$